{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9277116f-f64b-41ca-a8d1-8743b936d542",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import shap\n",
    "import lime\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score,explained_variance_score,mean_absolute_percentage_error\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "import matplotlib.cm as cm\n",
    "plt.rcParams['font.family'] = 'Times New Roman'\n",
    "plt.rcParams['font.size']=50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a411debf-681a-4961-9c1f-0a92747e666c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.read_excel('数据\\入模特征.xlsx')\n",
    "d = pd.read_excel('数据\\入模特征1.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "38861bfb-1184-4b0f-b4a8-d1af71f9f71e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>NAME</th>\n",
       "      <th>AvgSurfT_inst_X11</th>\n",
       "      <th>LST_Day_1km_X8</th>\n",
       "      <th>LST_Night_1km_fennie</th>\n",
       "      <th>soil_temperature_level_2_X9</th>\n",
       "      <th>soil_temperature_level_2_X10</th>\n",
       "      <th>soil_temperature_level_2_X12</th>\n",
       "      <th>soil_temperature_level_3_X8</th>\n",
       "      <th>soil_temperature_level_3_X9</th>\n",
       "      <th>...</th>\n",
       "      <th>SVAI_X7</th>\n",
       "      <th>SVAI_X9</th>\n",
       "      <th>WDRVI_X6</th>\n",
       "      <th>WDRVI_X8</th>\n",
       "      <th>WDVI_X6</th>\n",
       "      <th>WDVI_X7</th>\n",
       "      <th>WDVI_X8</th>\n",
       "      <th>WDVI_X9</th>\n",
       "      <th>wet_X9</th>\n",
       "      <th>亩产</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>410522</td>\n",
       "      <td>301.830072</td>\n",
       "      <td>14770.448120</td>\n",
       "      <td>13653.260039</td>\n",
       "      <td>291.679432</td>\n",
       "      <td>294.485981</td>\n",
       "      <td>301.239969</td>\n",
       "      <td>287.263466</td>\n",
       "      <td>288.780851</td>\n",
       "      <td>...</td>\n",
       "      <td>0.477002</td>\n",
       "      <td>0.502203</td>\n",
       "      <td>-0.270294</td>\n",
       "      <td>0.038094</td>\n",
       "      <td>0.312649</td>\n",
       "      <td>0.346212</td>\n",
       "      <td>0.380784</td>\n",
       "      <td>0.355850</td>\n",
       "      <td>-0.090609</td>\n",
       "      <td>7467.000138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>410421</td>\n",
       "      <td>301.881276</td>\n",
       "      <td>14811.998245</td>\n",
       "      <td>13724.830174</td>\n",
       "      <td>291.624652</td>\n",
       "      <td>294.462918</td>\n",
       "      <td>301.229017</td>\n",
       "      <td>287.616328</td>\n",
       "      <td>289.032764</td>\n",
       "      <td>...</td>\n",
       "      <td>0.457699</td>\n",
       "      <td>0.444113</td>\n",
       "      <td>-0.220879</td>\n",
       "      <td>0.020097</td>\n",
       "      <td>0.272293</td>\n",
       "      <td>0.321097</td>\n",
       "      <td>0.313054</td>\n",
       "      <td>0.308834</td>\n",
       "      <td>-0.088144</td>\n",
       "      <td>5972.666001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>411726</td>\n",
       "      <td>298.990822</td>\n",
       "      <td>15035.873561</td>\n",
       "      <td>13760.252546</td>\n",
       "      <td>291.141219</td>\n",
       "      <td>293.187955</td>\n",
       "      <td>298.777454</td>\n",
       "      <td>287.204259</td>\n",
       "      <td>288.763693</td>\n",
       "      <td>...</td>\n",
       "      <td>0.397070</td>\n",
       "      <td>0.406768</td>\n",
       "      <td>-0.273682</td>\n",
       "      <td>-0.176015</td>\n",
       "      <td>0.255247</td>\n",
       "      <td>0.291260</td>\n",
       "      <td>0.276982</td>\n",
       "      <td>0.277341</td>\n",
       "      <td>-0.107184</td>\n",
       "      <td>5687.791498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>410822</td>\n",
       "      <td>302.326105</td>\n",
       "      <td>14909.855104</td>\n",
       "      <td>13684.031442</td>\n",
       "      <td>291.393057</td>\n",
       "      <td>294.128413</td>\n",
       "      <td>300.920627</td>\n",
       "      <td>286.732262</td>\n",
       "      <td>288.209019</td>\n",
       "      <td>...</td>\n",
       "      <td>0.427460</td>\n",
       "      <td>0.392344</td>\n",
       "      <td>-0.305916</td>\n",
       "      <td>0.009030</td>\n",
       "      <td>0.277992</td>\n",
       "      <td>0.313744</td>\n",
       "      <td>0.335973</td>\n",
       "      <td>0.327689</td>\n",
       "      <td>-0.062063</td>\n",
       "      <td>7959.597222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>411082</td>\n",
       "      <td>302.665974</td>\n",
       "      <td>14764.439462</td>\n",
       "      <td>13685.820730</td>\n",
       "      <td>292.812644</td>\n",
       "      <td>295.863324</td>\n",
       "      <td>302.456445</td>\n",
       "      <td>288.378758</td>\n",
       "      <td>289.948855</td>\n",
       "      <td>...</td>\n",
       "      <td>0.495140</td>\n",
       "      <td>0.456340</td>\n",
       "      <td>-0.155182</td>\n",
       "      <td>0.027083</td>\n",
       "      <td>0.329961</td>\n",
       "      <td>0.356682</td>\n",
       "      <td>0.342203</td>\n",
       "      <td>0.353976</td>\n",
       "      <td>-0.074036</td>\n",
       "      <td>7662.496889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>97</td>\n",
       "      <td>411081</td>\n",
       "      <td>302.379726</td>\n",
       "      <td>14864.959928</td>\n",
       "      <td>13716.550674</td>\n",
       "      <td>292.036420</td>\n",
       "      <td>294.927847</td>\n",
       "      <td>301.419777</td>\n",
       "      <td>287.740673</td>\n",
       "      <td>289.256381</td>\n",
       "      <td>...</td>\n",
       "      <td>0.435926</td>\n",
       "      <td>0.427608</td>\n",
       "      <td>-0.222630</td>\n",
       "      <td>-0.033628</td>\n",
       "      <td>0.276777</td>\n",
       "      <td>0.307460</td>\n",
       "      <td>0.306785</td>\n",
       "      <td>0.308283</td>\n",
       "      <td>-0.090942</td>\n",
       "      <td>6613.422500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>98</td>\n",
       "      <td>411424</td>\n",
       "      <td>302.404313</td>\n",
       "      <td>14767.178119</td>\n",
       "      <td>13698.554350</td>\n",
       "      <td>293.142870</td>\n",
       "      <td>296.125191</td>\n",
       "      <td>302.061064</td>\n",
       "      <td>288.739608</td>\n",
       "      <td>290.273988</td>\n",
       "      <td>...</td>\n",
       "      <td>0.538458</td>\n",
       "      <td>0.518776</td>\n",
       "      <td>-0.049587</td>\n",
       "      <td>0.143995</td>\n",
       "      <td>0.356591</td>\n",
       "      <td>0.372768</td>\n",
       "      <td>0.380640</td>\n",
       "      <td>0.392420</td>\n",
       "      <td>-0.074186</td>\n",
       "      <td>7579.469809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>99</td>\n",
       "      <td>411724</td>\n",
       "      <td>300.297286</td>\n",
       "      <td>14800.494981</td>\n",
       "      <td>13753.180467</td>\n",
       "      <td>291.750152</td>\n",
       "      <td>293.718662</td>\n",
       "      <td>299.219180</td>\n",
       "      <td>287.779530</td>\n",
       "      <td>289.413163</td>\n",
       "      <td>...</td>\n",
       "      <td>0.525684</td>\n",
       "      <td>0.563379</td>\n",
       "      <td>0.082507</td>\n",
       "      <td>0.149694</td>\n",
       "      <td>0.389395</td>\n",
       "      <td>0.347135</td>\n",
       "      <td>0.345173</td>\n",
       "      <td>0.381115</td>\n",
       "      <td>-0.079914</td>\n",
       "      <td>5754.140236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>100</td>\n",
       "      <td>411324</td>\n",
       "      <td>300.250438</td>\n",
       "      <td>14870.302144</td>\n",
       "      <td>13755.946153</td>\n",
       "      <td>291.086794</td>\n",
       "      <td>293.369445</td>\n",
       "      <td>300.014684</td>\n",
       "      <td>287.141116</td>\n",
       "      <td>288.584741</td>\n",
       "      <td>...</td>\n",
       "      <td>0.467293</td>\n",
       "      <td>0.460888</td>\n",
       "      <td>-0.102704</td>\n",
       "      <td>0.005744</td>\n",
       "      <td>0.309025</td>\n",
       "      <td>0.317261</td>\n",
       "      <td>0.348503</td>\n",
       "      <td>0.307512</td>\n",
       "      <td>-0.097445</td>\n",
       "      <td>5467.128359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>101</td>\n",
       "      <td>410122</td>\n",
       "      <td>302.101448</td>\n",
       "      <td>14900.316284</td>\n",
       "      <td>13728.293344</td>\n",
       "      <td>292.494994</td>\n",
       "      <td>295.405081</td>\n",
       "      <td>301.660649</td>\n",
       "      <td>288.002027</td>\n",
       "      <td>289.590761</td>\n",
       "      <td>...</td>\n",
       "      <td>0.389068</td>\n",
       "      <td>0.390789</td>\n",
       "      <td>-0.356993</td>\n",
       "      <td>-0.172850</td>\n",
       "      <td>0.260427</td>\n",
       "      <td>0.290878</td>\n",
       "      <td>0.283795</td>\n",
       "      <td>0.284526</td>\n",
       "      <td>-0.095178</td>\n",
       "      <td>6637.200003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0    NAME  AvgSurfT_inst_X11  LST_Day_1km_X8   \n",
       "0             0  410522         301.830072    14770.448120  \\\n",
       "1             1  410421         301.881276    14811.998245   \n",
       "2             2  411726         298.990822    15035.873561   \n",
       "3             3  410822         302.326105    14909.855104   \n",
       "4             4  411082         302.665974    14764.439462   \n",
       "..          ...     ...                ...             ...   \n",
       "97           97  411081         302.379726    14864.959928   \n",
       "98           98  411424         302.404313    14767.178119   \n",
       "99           99  411724         300.297286    14800.494981   \n",
       "100         100  411324         300.250438    14870.302144   \n",
       "101         101  410122         302.101448    14900.316284   \n",
       "\n",
       "     LST_Night_1km_fennie  soil_temperature_level_2_X9   \n",
       "0            13653.260039                   291.679432  \\\n",
       "1            13724.830174                   291.624652   \n",
       "2            13760.252546                   291.141219   \n",
       "3            13684.031442                   291.393057   \n",
       "4            13685.820730                   292.812644   \n",
       "..                    ...                          ...   \n",
       "97           13716.550674                   292.036420   \n",
       "98           13698.554350                   293.142870   \n",
       "99           13753.180467                   291.750152   \n",
       "100          13755.946153                   291.086794   \n",
       "101          13728.293344                   292.494994   \n",
       "\n",
       "     soil_temperature_level_2_X10  soil_temperature_level_2_X12   \n",
       "0                      294.485981                    301.239969  \\\n",
       "1                      294.462918                    301.229017   \n",
       "2                      293.187955                    298.777454   \n",
       "3                      294.128413                    300.920627   \n",
       "4                      295.863324                    302.456445   \n",
       "..                            ...                           ...   \n",
       "97                     294.927847                    301.419777   \n",
       "98                     296.125191                    302.061064   \n",
       "99                     293.718662                    299.219180   \n",
       "100                    293.369445                    300.014684   \n",
       "101                    295.405081                    301.660649   \n",
       "\n",
       "     soil_temperature_level_3_X8  soil_temperature_level_3_X9  ...   SVAI_X7   \n",
       "0                     287.263466                   288.780851  ...  0.477002  \\\n",
       "1                     287.616328                   289.032764  ...  0.457699   \n",
       "2                     287.204259                   288.763693  ...  0.397070   \n",
       "3                     286.732262                   288.209019  ...  0.427460   \n",
       "4                     288.378758                   289.948855  ...  0.495140   \n",
       "..                           ...                          ...  ...       ...   \n",
       "97                    287.740673                   289.256381  ...  0.435926   \n",
       "98                    288.739608                   290.273988  ...  0.538458   \n",
       "99                    287.779530                   289.413163  ...  0.525684   \n",
       "100                   287.141116                   288.584741  ...  0.467293   \n",
       "101                   288.002027                   289.590761  ...  0.389068   \n",
       "\n",
       "      SVAI_X9  WDRVI_X6  WDRVI_X8   WDVI_X6   WDVI_X7   WDVI_X8   WDVI_X9   \n",
       "0    0.502203 -0.270294  0.038094  0.312649  0.346212  0.380784  0.355850  \\\n",
       "1    0.444113 -0.220879  0.020097  0.272293  0.321097  0.313054  0.308834   \n",
       "2    0.406768 -0.273682 -0.176015  0.255247  0.291260  0.276982  0.277341   \n",
       "3    0.392344 -0.305916  0.009030  0.277992  0.313744  0.335973  0.327689   \n",
       "4    0.456340 -0.155182  0.027083  0.329961  0.356682  0.342203  0.353976   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "97   0.427608 -0.222630 -0.033628  0.276777  0.307460  0.306785  0.308283   \n",
       "98   0.518776 -0.049587  0.143995  0.356591  0.372768  0.380640  0.392420   \n",
       "99   0.563379  0.082507  0.149694  0.389395  0.347135  0.345173  0.381115   \n",
       "100  0.460888 -0.102704  0.005744  0.309025  0.317261  0.348503  0.307512   \n",
       "101  0.390789 -0.356993 -0.172850  0.260427  0.290878  0.283795  0.284526   \n",
       "\n",
       "       wet_X9           亩产  \n",
       "0   -0.090609  7467.000138  \n",
       "1   -0.088144  5972.666001  \n",
       "2   -0.107184  5687.791498  \n",
       "3   -0.062063  7959.597222  \n",
       "4   -0.074036  7662.496889  \n",
       "..        ...          ...  \n",
       "97  -0.090942  6613.422500  \n",
       "98  -0.074186  7579.469809  \n",
       "99  -0.079914  5754.140236  \n",
       "100 -0.097445  5467.128359  \n",
       "101 -0.095178  6637.200003  \n",
       "\n",
       "[102 rows x 62 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "28347201-d5c7-49b6-a129-0adc4a6a19bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=df.drop(columns=['Unnamed: 0'])\n",
    "d1=d.drop(columns=['Unnamed: 0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e9c86308-02eb-4dc0-94cd-4a8582681044",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_train =df1.drop(columns = [\"亩产\"])\n",
    "y_train = df1['亩产']\n",
    "x_test =d1.drop(columns = [\"亩产\"])\n",
    "y_test = d1['亩产']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2e9ba401-cb31-4abf-bb7d-8b371122b737",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = xgb.XGBRegressor(n_estimators=150,\n",
    "                                 max_depth=5,\n",
    "                                 learning_rate=0.1,\n",
    "                                 random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6b1850fb-cdc2-499e-811a-c6d3a5d8d5d6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "xgboost_model_fit = model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a999d546-63cb-4707-bc00-decefca0d8e3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[13:06:46] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0cec3277c4d9d0165-1\\xgboost\\xgboost-ci-windows\\src\\c_api\\c_api.cc:1240: Saving into deprecated binary model format, please consider using `json` or `ubj`. Model format will default to JSON in XGBoost 2.2 if not specified.\n"
     ]
    }
   ],
   "source": [
    "explainer = shap.TreeExplainer(xgboost_model_fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ac3566ce-464d-42e0-b403-3ef956578d02",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "shap_values = explainer.shap_values(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ec60e598-33ec-44c9-ba3b-c7a428997673",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-224.22195  ,  170.55058  ,    4.183008 , ...,  454.8096   ,\n",
       "          79.13559  ,   62.6566   ],\n",
       "       [-529.85876  ,  196.8837   ,   20.768879 , ...,  276.64795  ,\n",
       "         114.68752  ,  105.63411  ],\n",
       "       [ 373.48022  , -121.62694  ,  -37.31802  , ..., -314.6787   ,\n",
       "        -197.75034  ,    2.5015287],\n",
       "       ...,\n",
       "       [ 123.588326 ,    4.639418 ,   -2.8667557, ...,  420.91724  ,\n",
       "          95.23867  ,  103.96476  ],\n",
       "       [ -55.773117 ,   -4.677651 ,   89.81136  , ...,  453.37064  ,\n",
       "         110.2197   ,   12.0194845],\n",
       "       [-353.81967  ,  460.12195  ,   24.781406 , ..., -430.71838  ,\n",
       "        -202.01483  ,   54.503754 ]], dtype=float32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap_values "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b97867a-eb9a-4ef6-bbf9-49d27cce4e1b",
   "metadata": {},
   "source": [
    "#### 验证shap值和预测结果的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "975c940d-b1a8-4800-be38-a0cda4b4ccbc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>feature_value</th>\n",
       "      <th>shap_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NAME</td>\n",
       "      <td>410926.000000</td>\n",
       "      <td>139.440414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AvgSurfT_inst_X11</td>\n",
       "      <td>301.802450</td>\n",
       "      <td>282.699677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LST_Day_1km_X8</td>\n",
       "      <td>14805.622165</td>\n",
       "      <td>-29.288834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LST_Night_1km_fennie</td>\n",
       "      <td>13711.910629</td>\n",
       "      <td>63.296909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>soil_temperature_level_2_X9</td>\n",
       "      <td>291.557327</td>\n",
       "      <td>13.815547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>soil_temperature_level_2_X10</td>\n",
       "      <td>294.913916</td>\n",
       "      <td>-0.919878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>soil_temperature_level_2_X12</td>\n",
       "      <td>301.156543</td>\n",
       "      <td>49.266586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>soil_temperature_level_3_X8</td>\n",
       "      <td>287.274575</td>\n",
       "      <td>7.703931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>soil_temperature_level_3_X9</td>\n",
       "      <td>288.805227</td>\n",
       "      <td>-0.931691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>soil_temperature_level_3_X10</td>\n",
       "      <td>290.851001</td>\n",
       "      <td>5.246749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>soil_temperature_level_1_X10</td>\n",
       "      <td>296.541585</td>\n",
       "      <td>-1.341685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>SoilMoi0_10cm_inst_X8</td>\n",
       "      <td>23.582840</td>\n",
       "      <td>37.448971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>SoilMoi0_10cm_inst_X9</td>\n",
       "      <td>20.312242</td>\n",
       "      <td>-4.298964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>SoilMoi0_10cm_inst_X10</td>\n",
       "      <td>15.773918</td>\n",
       "      <td>3.482481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>SoilMoi10_40cm_inst_X8</td>\n",
       "      <td>74.679514</td>\n",
       "      <td>-5.839984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>SoilMoi10_40cm_inst_X9</td>\n",
       "      <td>66.992819</td>\n",
       "      <td>-0.125264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>SoilMoi10_40cm_inst_X10</td>\n",
       "      <td>52.888282</td>\n",
       "      <td>-0.301332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>VDVI_X12</td>\n",
       "      <td>0.021631</td>\n",
       "      <td>-59.094448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>EVI_X8</td>\n",
       "      <td>0.452189</td>\n",
       "      <td>-2.369341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>SR_X9</td>\n",
       "      <td>5.011694</td>\n",
       "      <td>-46.334862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>DVI_X6</td>\n",
       "      <td>0.159828</td>\n",
       "      <td>-13.427817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>DVI_X7</td>\n",
       "      <td>0.228017</td>\n",
       "      <td>-16.343983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>DVI_X8</td>\n",
       "      <td>0.270910</td>\n",
       "      <td>0.936193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>DVI_X9</td>\n",
       "      <td>0.280407</td>\n",
       "      <td>7.310988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>EVI_X6</td>\n",
       "      <td>0.268119</td>\n",
       "      <td>-23.820158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>EVI_X7</td>\n",
       "      <td>0.390467</td>\n",
       "      <td>0.628359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>EVI_X9</td>\n",
       "      <td>0.482951</td>\n",
       "      <td>-4.105835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>EVI2_X6</td>\n",
       "      <td>0.254044</td>\n",
       "      <td>0.063231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>EVI2_X7</td>\n",
       "      <td>0.371465</td>\n",
       "      <td>2.989821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>EVI2_X8</td>\n",
       "      <td>0.430292</td>\n",
       "      <td>10.457395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>EVI2_X9</td>\n",
       "      <td>0.461786</td>\n",
       "      <td>-6.063097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>GCVI_X6</td>\n",
       "      <td>1.605794</td>\n",
       "      <td>7.372938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>GCVI_X7</td>\n",
       "      <td>2.580162</td>\n",
       "      <td>-9.234424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>GCVI_X9</td>\n",
       "      <td>3.825117</td>\n",
       "      <td>20.049536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>GNDVI_X6</td>\n",
       "      <td>0.439279</td>\n",
       "      <td>10.995112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Lai_500m_X6</td>\n",
       "      <td>4.749229</td>\n",
       "      <td>-3.946393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Lai_500m_X7</td>\n",
       "      <td>9.605945</td>\n",
       "      <td>0.562212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Lai_500m_X8</td>\n",
       "      <td>15.747539</td>\n",
       "      <td>6.310420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Lai_500m_X9</td>\n",
       "      <td>18.318241</td>\n",
       "      <td>-42.090660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>MCARI_X6</td>\n",
       "      <td>-0.095959</td>\n",
       "      <td>-7.422985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>MCARI_X7</td>\n",
       "      <td>0.022530</td>\n",
       "      <td>-32.239040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>MCARI_X8</td>\n",
       "      <td>0.137190</td>\n",
       "      <td>-0.652164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>MCARI_X9</td>\n",
       "      <td>0.217759</td>\n",
       "      <td>-8.619356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>MSR_X6</td>\n",
       "      <td>0.626495</td>\n",
       "      <td>1.750894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>MSR_X7</td>\n",
       "      <td>1.219404</td>\n",
       "      <td>21.009193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>MSR_X8</td>\n",
       "      <td>1.389483</td>\n",
       "      <td>17.191540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>NDVI_X6</td>\n",
       "      <td>0.395715</td>\n",
       "      <td>4.853804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>NDVI_X7</td>\n",
       "      <td>0.558388</td>\n",
       "      <td>-0.641839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>OSAVI_X6</td>\n",
       "      <td>0.452794</td>\n",
       "      <td>-1.671667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>OSAVI_X7</td>\n",
       "      <td>0.641908</td>\n",
       "      <td>-0.865383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>OSAVI_X9</td>\n",
       "      <td>0.773268</td>\n",
       "      <td>5.763987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>SVAI_X7</td>\n",
       "      <td>0.376536</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>SVAI_X9</td>\n",
       "      <td>0.457085</td>\n",
       "      <td>-0.345605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>WDRVI_X6</td>\n",
       "      <td>-0.408118</td>\n",
       "      <td>-0.103942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>WDRVI_X8</td>\n",
       "      <td>-0.097425</td>\n",
       "      <td>-1.290622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>WDVI_X6</td>\n",
       "      <td>0.220377</td>\n",
       "      <td>-90.215874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>WDVI_X7</td>\n",
       "      <td>0.273100</td>\n",
       "      <td>-8.420839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>WDVI_X8</td>\n",
       "      <td>0.315481</td>\n",
       "      <td>481.649689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>WDVI_X9</td>\n",
       "      <td>0.315356</td>\n",
       "      <td>116.754951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>wet_X9</td>\n",
       "      <td>-0.105645</td>\n",
       "      <td>-20.475628</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         feature  feature_value  shap_value\n",
       "0                           NAME  410926.000000  139.440414\n",
       "1              AvgSurfT_inst_X11     301.802450  282.699677\n",
       "2                 LST_Day_1km_X8   14805.622165  -29.288834\n",
       "3           LST_Night_1km_fennie   13711.910629   63.296909\n",
       "4    soil_temperature_level_2_X9     291.557327   13.815547\n",
       "5   soil_temperature_level_2_X10     294.913916   -0.919878\n",
       "6   soil_temperature_level_2_X12     301.156543   49.266586\n",
       "7    soil_temperature_level_3_X8     287.274575    7.703931\n",
       "8    soil_temperature_level_3_X9     288.805227   -0.931691\n",
       "9   soil_temperature_level_3_X10     290.851001    5.246749\n",
       "10  soil_temperature_level_1_X10     296.541585   -1.341685\n",
       "11         SoilMoi0_10cm_inst_X8      23.582840   37.448971\n",
       "12         SoilMoi0_10cm_inst_X9      20.312242   -4.298964\n",
       "13        SoilMoi0_10cm_inst_X10      15.773918    3.482481\n",
       "14        SoilMoi10_40cm_inst_X8      74.679514   -5.839984\n",
       "15        SoilMoi10_40cm_inst_X9      66.992819   -0.125264\n",
       "16       SoilMoi10_40cm_inst_X10      52.888282   -0.301332\n",
       "17                      VDVI_X12       0.021631  -59.094448\n",
       "18                        EVI_X8       0.452189   -2.369341\n",
       "19                         SR_X9       5.011694  -46.334862\n",
       "20                        DVI_X6       0.159828  -13.427817\n",
       "21                        DVI_X7       0.228017  -16.343983\n",
       "22                        DVI_X8       0.270910    0.936193\n",
       "23                        DVI_X9       0.280407    7.310988\n",
       "24                        EVI_X6       0.268119  -23.820158\n",
       "25                        EVI_X7       0.390467    0.628359\n",
       "26                        EVI_X9       0.482951   -4.105835\n",
       "27                       EVI2_X6       0.254044    0.063231\n",
       "28                       EVI2_X7       0.371465    2.989821\n",
       "29                       EVI2_X8       0.430292   10.457395\n",
       "30                       EVI2_X9       0.461786   -6.063097\n",
       "31                       GCVI_X6       1.605794    7.372938\n",
       "32                       GCVI_X7       2.580162   -9.234424\n",
       "33                       GCVI_X9       3.825117   20.049536\n",
       "34                      GNDVI_X6       0.439279   10.995112\n",
       "35                   Lai_500m_X6       4.749229   -3.946393\n",
       "36                   Lai_500m_X7       9.605945    0.562212\n",
       "37                   Lai_500m_X8      15.747539    6.310420\n",
       "38                   Lai_500m_X9      18.318241  -42.090660\n",
       "39                      MCARI_X6      -0.095959   -7.422985\n",
       "40                      MCARI_X7       0.022530  -32.239040\n",
       "41                      MCARI_X8       0.137190   -0.652164\n",
       "42                      MCARI_X9       0.217759   -8.619356\n",
       "43                        MSR_X6       0.626495    1.750894\n",
       "44                        MSR_X7       1.219404   21.009193\n",
       "45                        MSR_X8       1.389483   17.191540\n",
       "46                       NDVI_X6       0.395715    4.853804\n",
       "47                       NDVI_X7       0.558388   -0.641839\n",
       "48                      OSAVI_X6       0.452794   -1.671667\n",
       "49                      OSAVI_X7       0.641908   -0.865383\n",
       "50                      OSAVI_X9       0.773268    5.763987\n",
       "51                       SVAI_X7       0.376536    0.000000\n",
       "52                       SVAI_X9       0.457085   -0.345605\n",
       "53                      WDRVI_X6      -0.408118   -0.103942\n",
       "54                      WDRVI_X8      -0.097425   -1.290622\n",
       "55                       WDVI_X6       0.220377  -90.215874\n",
       "56                       WDVI_X7       0.273100   -8.420839\n",
       "57                       WDVI_X8       0.315481  481.649689\n",
       "58                       WDVI_X9       0.315356  116.754951\n",
       "59                        wet_X9      -0.105645  -20.475628"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j = 10\n",
    "y_base = explainer.expected_value\n",
    "y_pred_xgboost = xgboost_model_fit.predict(x_test)\n",
    "player_explainer = pd.DataFrame()\n",
    "player_explainer['feature'] = x_test.columns\n",
    "player_explainer['feature_value'] = x_test.iloc[j].values\n",
    "player_explainer['shap_value'] = shap_values[j]\n",
    "pd.set_option('display.max_rows', None)\n",
    "player_explainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ce7acd6a-a093-45e5-a77a-167e7bf63bec",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6152.8853"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3c61a5b1-2aa7-4369-9383-b77bfdac47fc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_base + sum_of_shap_values_1: 7029.09\n",
      "y_pred: 7029.09\n",
      "y: 6955.21\n"
     ]
    }
   ],
   "source": [
    "print('y_base + sum_of_shap_values_1: %.2f'%(y_base + player_explainer['shap_value'].sum()))#####预测值公式：基础值加上所有shap值\n",
    "print('y_pred: %.2f'%(y_pred_xgboost[j]))\n",
    "print('y: %.2f'%(y_test.iloc[j]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2343a75b-df4b-42cb-8a47-0387e5413ad3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "result_1 = pd.DataFrame()\n",
    "result_1['shap预测值'] = np.sum(shap_values,axis = 1) + y_base\n",
    "result_1['xgb预测值'] = y_pred_xgboost###预测值\n",
    "result_1['真实值'] = y_test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5efd002c-5a06-434a-bd8d-7ec2bd0e38cc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>shap预测值</th>\n",
       "      <th>xgb预测值</th>\n",
       "      <th>真实值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7031.935547</td>\n",
       "      <td>7031.933594</td>\n",
       "      <td>7467.000138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6393.282227</td>\n",
       "      <td>6393.281250</td>\n",
       "      <td>5972.666001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5632.630371</td>\n",
       "      <td>5632.632324</td>\n",
       "      <td>5687.791498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7307.734375</td>\n",
       "      <td>7307.732422</td>\n",
       "      <td>7959.597222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7662.821289</td>\n",
       "      <td>7662.821289</td>\n",
       "      <td>7662.496889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7560.841797</td>\n",
       "      <td>7560.840332</td>\n",
       "      <td>7900.921659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7451.310059</td>\n",
       "      <td>7451.311035</td>\n",
       "      <td>7580.668764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3976.963379</td>\n",
       "      <td>3976.965576</td>\n",
       "      <td>3607.373664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5898.644043</td>\n",
       "      <td>5898.646484</td>\n",
       "      <td>6235.856967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6570.583008</td>\n",
       "      <td>6570.583984</td>\n",
       "      <td>4920.459120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7029.093262</td>\n",
       "      <td>7029.092285</td>\n",
       "      <td>6955.205241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7402.962891</td>\n",
       "      <td>7402.960938</td>\n",
       "      <td>7403.843878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7452.396973</td>\n",
       "      <td>7452.398438</td>\n",
       "      <td>7528.365532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5297.129883</td>\n",
       "      <td>5297.130371</td>\n",
       "      <td>4417.144907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4257.396484</td>\n",
       "      <td>4257.395996</td>\n",
       "      <td>4155.027793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4163.113281</td>\n",
       "      <td>4163.113770</td>\n",
       "      <td>4602.146558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5934.594727</td>\n",
       "      <td>5934.595215</td>\n",
       "      <td>5094.642857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5045.160645</td>\n",
       "      <td>5045.159668</td>\n",
       "      <td>4295.385406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>7571.148438</td>\n",
       "      <td>7571.148438</td>\n",
       "      <td>7913.079470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6879.195801</td>\n",
       "      <td>6879.194336</td>\n",
       "      <td>7067.651475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>7075.977539</td>\n",
       "      <td>7075.975586</td>\n",
       "      <td>7322.816243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>6242.027832</td>\n",
       "      <td>6242.026855</td>\n",
       "      <td>6090.645161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7583.140625</td>\n",
       "      <td>7583.139160</td>\n",
       "      <td>8044.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6450.745117</td>\n",
       "      <td>6450.743652</td>\n",
       "      <td>6057.530560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4628.364258</td>\n",
       "      <td>4628.363770</td>\n",
       "      <td>5012.325390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7499.154785</td>\n",
       "      <td>7499.154297</td>\n",
       "      <td>7771.499988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4601.448242</td>\n",
       "      <td>4601.446289</td>\n",
       "      <td>4703.999023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4382.453125</td>\n",
       "      <td>4382.455078</td>\n",
       "      <td>4973.836185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4812.153320</td>\n",
       "      <td>4812.154297</td>\n",
       "      <td>4829.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4404.453125</td>\n",
       "      <td>4404.453613</td>\n",
       "      <td>4174.827337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>5765.733887</td>\n",
       "      <td>5765.732910</td>\n",
       "      <td>3994.140919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4396.289551</td>\n",
       "      <td>4396.289062</td>\n",
       "      <td>4704.232534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>7414.076172</td>\n",
       "      <td>7414.078125</td>\n",
       "      <td>7552.074109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>6765.001953</td>\n",
       "      <td>6765.001465</td>\n",
       "      <td>7973.357166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4717.536621</td>\n",
       "      <td>4717.538086</td>\n",
       "      <td>4827.102804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>7396.326172</td>\n",
       "      <td>7396.326660</td>\n",
       "      <td>7369.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>7372.705078</td>\n",
       "      <td>7372.705078</td>\n",
       "      <td>8068.112967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>4430.463867</td>\n",
       "      <td>4430.464355</td>\n",
       "      <td>4352.341822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>7142.912109</td>\n",
       "      <td>7142.910156</td>\n",
       "      <td>7229.850041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>5239.359375</td>\n",
       "      <td>5239.360352</td>\n",
       "      <td>5783.108859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>7289.489746</td>\n",
       "      <td>7289.490723</td>\n",
       "      <td>7423.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>7255.498047</td>\n",
       "      <td>7255.498535</td>\n",
       "      <td>7167.503934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>7041.248047</td>\n",
       "      <td>7041.245117</td>\n",
       "      <td>7161.741178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>7593.677246</td>\n",
       "      <td>7593.676758</td>\n",
       "      <td>7748.874460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>7400.789551</td>\n",
       "      <td>7400.787109</td>\n",
       "      <td>7902.608986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>7143.921387</td>\n",
       "      <td>7143.920410</td>\n",
       "      <td>7816.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>6541.867676</td>\n",
       "      <td>6541.865723</td>\n",
       "      <td>6754.134713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>6139.372559</td>\n",
       "      <td>6139.375000</td>\n",
       "      <td>5942.024339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>6947.122070</td>\n",
       "      <td>6947.122559</td>\n",
       "      <td>6911.523612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>4355.847168</td>\n",
       "      <td>4355.849121</td>\n",
       "      <td>4436.270566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>5168.244629</td>\n",
       "      <td>5168.245117</td>\n",
       "      <td>5275.476388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>7359.990723</td>\n",
       "      <td>7359.989746</td>\n",
       "      <td>7195.856555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>4196.038086</td>\n",
       "      <td>4196.037598</td>\n",
       "      <td>3963.853159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>7514.363770</td>\n",
       "      <td>7514.363281</td>\n",
       "      <td>7559.231856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>6842.391602</td>\n",
       "      <td>6842.393066</td>\n",
       "      <td>7582.026978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>6152.353516</td>\n",
       "      <td>6152.355957</td>\n",
       "      <td>4853.310121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>4619.449707</td>\n",
       "      <td>4619.451660</td>\n",
       "      <td>4576.994060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>7107.862305</td>\n",
       "      <td>7107.861816</td>\n",
       "      <td>6952.782368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>7431.725586</td>\n",
       "      <td>7431.727051</td>\n",
       "      <td>7398.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>7494.908691</td>\n",
       "      <td>7494.908691</td>\n",
       "      <td>7549.241972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>5824.129395</td>\n",
       "      <td>5824.128906</td>\n",
       "      <td>7114.927966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>6021.964844</td>\n",
       "      <td>6021.964355</td>\n",
       "      <td>6903.728591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>6389.746582</td>\n",
       "      <td>6389.746094</td>\n",
       "      <td>7136.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>4840.547363</td>\n",
       "      <td>4840.548340</td>\n",
       "      <td>4326.861279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>6600.953613</td>\n",
       "      <td>6600.952637</td>\n",
       "      <td>6711.924439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>7072.174805</td>\n",
       "      <td>7072.171875</td>\n",
       "      <td>6759.445867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>6470.138184</td>\n",
       "      <td>6470.137207</td>\n",
       "      <td>6614.710873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>7402.095215</td>\n",
       "      <td>7402.093262</td>\n",
       "      <td>8232.639986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>6023.530762</td>\n",
       "      <td>6023.530273</td>\n",
       "      <td>5834.761322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>7409.514648</td>\n",
       "      <td>7409.514648</td>\n",
       "      <td>7567.499988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>7528.792969</td>\n",
       "      <td>7528.792480</td>\n",
       "      <td>8011.521955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>7401.479492</td>\n",
       "      <td>7401.479004</td>\n",
       "      <td>7529.649477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>7455.140137</td>\n",
       "      <td>7455.140137</td>\n",
       "      <td>7611.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>7441.169922</td>\n",
       "      <td>7441.171387</td>\n",
       "      <td>7591.177278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>4956.319824</td>\n",
       "      <td>4956.321289</td>\n",
       "      <td>4668.818529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>7487.291016</td>\n",
       "      <td>7487.290527</td>\n",
       "      <td>7525.460068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>4631.237305</td>\n",
       "      <td>4631.238770</td>\n",
       "      <td>5073.083638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>6696.004395</td>\n",
       "      <td>6696.005859</td>\n",
       "      <td>6642.381576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>5900.212891</td>\n",
       "      <td>5900.213867</td>\n",
       "      <td>6468.299989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>4977.337891</td>\n",
       "      <td>4977.338379</td>\n",
       "      <td>4383.449997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>3665.892578</td>\n",
       "      <td>3665.893799</td>\n",
       "      <td>3315.477429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>7384.694336</td>\n",
       "      <td>7384.692871</td>\n",
       "      <td>7537.911527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>6095.793457</td>\n",
       "      <td>6095.793945</td>\n",
       "      <td>7035.697699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>6256.450684</td>\n",
       "      <td>6256.449707</td>\n",
       "      <td>6179.549995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>7526.495605</td>\n",
       "      <td>7526.493652</td>\n",
       "      <td>7150.926063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>7691.564941</td>\n",
       "      <td>7691.562988</td>\n",
       "      <td>7692.480602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>6516.312988</td>\n",
       "      <td>6516.312988</td>\n",
       "      <td>5351.581312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>4062.244629</td>\n",
       "      <td>4062.247070</td>\n",
       "      <td>3398.081606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>7184.084961</td>\n",
       "      <td>7184.084473</td>\n",
       "      <td>6924.185703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>7452.315918</td>\n",
       "      <td>7452.314941</td>\n",
       "      <td>7875.171429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>6180.640625</td>\n",
       "      <td>6180.638672</td>\n",
       "      <td>6047.464093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>5143.658203</td>\n",
       "      <td>5143.658203</td>\n",
       "      <td>5800.797857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>4666.815430</td>\n",
       "      <td>4666.815918</td>\n",
       "      <td>4722.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>5152.003418</td>\n",
       "      <td>5152.003906</td>\n",
       "      <td>4832.340243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>7469.453613</td>\n",
       "      <td>7469.454590</td>\n",
       "      <td>7566.087445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>7113.933105</td>\n",
       "      <td>7113.931641</td>\n",
       "      <td>6925.351492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>7343.841309</td>\n",
       "      <td>7343.840820</td>\n",
       "      <td>7524.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>7483.548828</td>\n",
       "      <td>7483.548340</td>\n",
       "      <td>6613.422500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>7479.232422</td>\n",
       "      <td>7479.232422</td>\n",
       "      <td>7579.469809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>6348.061523</td>\n",
       "      <td>6348.062012</td>\n",
       "      <td>5754.140236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>6105.768066</td>\n",
       "      <td>6105.768066</td>\n",
       "      <td>5467.128359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>6047.032715</td>\n",
       "      <td>6047.032715</td>\n",
       "      <td>6637.200003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         shap预测值       xgb预测值          真实值\n",
       "0    7031.935547  7031.933594  7467.000138\n",
       "1    6393.282227  6393.281250  5972.666001\n",
       "2    5632.630371  5632.632324  5687.791498\n",
       "3    7307.734375  7307.732422  7959.597222\n",
       "4    7662.821289  7662.821289  7662.496889\n",
       "5    7560.841797  7560.840332  7900.921659\n",
       "6    7451.310059  7451.311035  7580.668764\n",
       "7    3976.963379  3976.965576  3607.373664\n",
       "8    5898.644043  5898.646484  6235.856967\n",
       "9    6570.583008  6570.583984  4920.459120\n",
       "10   7029.093262  7029.092285  6955.205241\n",
       "11   7402.962891  7402.960938  7403.843878\n",
       "12   7452.396973  7452.398438  7528.365532\n",
       "13   5297.129883  5297.130371  4417.144907\n",
       "14   4257.396484  4257.395996  4155.027793\n",
       "15   4163.113281  4163.113770  4602.146558\n",
       "16   5934.594727  5934.595215  5094.642857\n",
       "17   5045.160645  5045.159668  4295.385406\n",
       "18   7571.148438  7571.148438  7913.079470\n",
       "19   6879.195801  6879.194336  7067.651475\n",
       "20   7075.977539  7075.975586  7322.816243\n",
       "21   6242.027832  6242.026855  6090.645161\n",
       "22   7583.140625  7583.139160  8044.050000\n",
       "23   6450.745117  6450.743652  6057.530560\n",
       "24   4628.364258  4628.363770  5012.325390\n",
       "25   7499.154785  7499.154297  7771.499988\n",
       "26   4601.448242  4601.446289  4703.999023\n",
       "27   4382.453125  4382.455078  4973.836185\n",
       "28   4812.153320  4812.154297  4829.400000\n",
       "29   4404.453125  4404.453613  4174.827337\n",
       "30   5765.733887  5765.732910  3994.140919\n",
       "31   4396.289551  4396.289062  4704.232534\n",
       "32   7414.076172  7414.078125  7552.074109\n",
       "33   6765.001953  6765.001465  7973.357166\n",
       "34   4717.536621  4717.538086  4827.102804\n",
       "35   7396.326172  7396.326660  7369.200000\n",
       "36   7372.705078  7372.705078  8068.112967\n",
       "37   4430.463867  4430.464355  4352.341822\n",
       "38   7142.912109  7142.910156  7229.850041\n",
       "39   5239.359375  5239.360352  5783.108859\n",
       "40   7289.489746  7289.490723  7423.200000\n",
       "41   7255.498047  7255.498535  7167.503934\n",
       "42   7041.248047  7041.245117  7161.741178\n",
       "43   7593.677246  7593.676758  7748.874460\n",
       "44   7400.789551  7400.787109  7902.608986\n",
       "45   7143.921387  7143.920410  7816.350000\n",
       "46   6541.867676  6541.865723  6754.134713\n",
       "47   6139.372559  6139.375000  5942.024339\n",
       "48   6947.122070  6947.122559  6911.523612\n",
       "49   4355.847168  4355.849121  4436.270566\n",
       "50   5168.244629  5168.245117  5275.476388\n",
       "51   7359.990723  7359.989746  7195.856555\n",
       "52   4196.038086  4196.037598  3963.853159\n",
       "53   7514.363770  7514.363281  7559.231856\n",
       "54   6842.391602  6842.393066  7582.026978\n",
       "55   6152.353516  6152.355957  4853.310121\n",
       "56   4619.449707  4619.451660  4576.994060\n",
       "57   7107.862305  7107.861816  6952.782368\n",
       "58   7431.725586  7431.727051  7398.450000\n",
       "59   7494.908691  7494.908691  7549.241972\n",
       "60   5824.129395  5824.128906  7114.927966\n",
       "61   6021.964844  6021.964355  6903.728591\n",
       "62   6389.746582  6389.746094  7136.250000\n",
       "63   4840.547363  4840.548340  4326.861279\n",
       "64   6600.953613  6600.952637  6711.924439\n",
       "65   7072.174805  7072.171875  6759.445867\n",
       "66   6470.138184  6470.137207  6614.710873\n",
       "67   7402.095215  7402.093262  8232.639986\n",
       "68   6023.530762  6023.530273  5834.761322\n",
       "69   7409.514648  7409.514648  7567.499988\n",
       "70   7528.792969  7528.792480  8011.521955\n",
       "71   7401.479492  7401.479004  7529.649477\n",
       "72   7455.140137  7455.140137  7611.700000\n",
       "73   7441.169922  7441.171387  7591.177278\n",
       "74   4956.319824  4956.321289  4668.818529\n",
       "75   7487.291016  7487.290527  7525.460068\n",
       "76   4631.237305  4631.238770  5073.083638\n",
       "77   6696.004395  6696.005859  6642.381576\n",
       "78   5900.212891  5900.213867  6468.299989\n",
       "79   4977.337891  4977.338379  4383.449997\n",
       "80   3665.892578  3665.893799  3315.477429\n",
       "81   7384.694336  7384.692871  7537.911527\n",
       "82   6095.793457  6095.793945  7035.697699\n",
       "83   6256.450684  6256.449707  6179.549995\n",
       "84   7526.495605  7526.493652  7150.926063\n",
       "85   7691.564941  7691.562988  7692.480602\n",
       "86   6516.312988  6516.312988  5351.581312\n",
       "87   4062.244629  4062.247070  3398.081606\n",
       "88   7184.084961  7184.084473  6924.185703\n",
       "89   7452.315918  7452.314941  7875.171429\n",
       "90   6180.640625  6180.638672  6047.464093\n",
       "91   5143.658203  5143.658203  5800.797857\n",
       "92   4666.815430  4666.815918  4722.222222\n",
       "93   5152.003418  5152.003906  4832.340243\n",
       "94   7469.453613  7469.454590  7566.087445\n",
       "95   7113.933105  7113.931641  6925.351492\n",
       "96   7343.841309  7343.840820  7524.000000\n",
       "97   7483.548828  7483.548340  6613.422500\n",
       "98   7479.232422  7479.232422  7579.469809\n",
       "99   6348.061523  6348.062012  5754.140236\n",
       "100  6105.768066  6105.768066  5467.128359\n",
       "101  6047.032715  6047.032715  6637.200003"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', None)\n",
    "result_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f8f1241a-31f5-43e2-ace7-2ebe4e4ac183",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "shap预测值    7029.093262\n",
       "xgb预测值     7029.092285\n",
       "真实值        6955.205241\n",
       "Name: 10, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_1.iloc[j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a7404c1a-0d13-42c1-bdfe-d7951a236755",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NAME                            410926.000000\n",
       "AvgSurfT_inst_X11                  301.802450\n",
       "LST_Day_1km_X8                   14805.622165\n",
       "LST_Night_1km_fennie             13711.910629\n",
       "soil_temperature_level_2_X9        291.557327\n",
       "soil_temperature_level_2_X10       294.913916\n",
       "soil_temperature_level_2_X12       301.156543\n",
       "soil_temperature_level_3_X8        287.274575\n",
       "soil_temperature_level_3_X9        288.805227\n",
       "soil_temperature_level_3_X10       290.851001\n",
       "soil_temperature_level_1_X10       296.541585\n",
       "SoilMoi0_10cm_inst_X8               23.582840\n",
       "SoilMoi0_10cm_inst_X9               20.312242\n",
       "SoilMoi0_10cm_inst_X10              15.773918\n",
       "SoilMoi10_40cm_inst_X8              74.679514\n",
       "SoilMoi10_40cm_inst_X9              66.992819\n",
       "SoilMoi10_40cm_inst_X10             52.888282\n",
       "VDVI_X12                             0.021631\n",
       "EVI_X8                               0.452189\n",
       "SR_X9                                5.011694\n",
       "DVI_X6                               0.159828\n",
       "DVI_X7                               0.228017\n",
       "DVI_X8                               0.270910\n",
       "DVI_X9                               0.280407\n",
       "EVI_X6                               0.268119\n",
       "EVI_X7                               0.390467\n",
       "EVI_X9                               0.482951\n",
       "EVI2_X6                              0.254044\n",
       "EVI2_X7                              0.371465\n",
       "EVI2_X8                              0.430292\n",
       "EVI2_X9                              0.461786\n",
       "GCVI_X6                              1.605794\n",
       "GCVI_X7                              2.580162\n",
       "GCVI_X9                              3.825117\n",
       "GNDVI_X6                             0.439279\n",
       "Lai_500m_X6                          4.749229\n",
       "Lai_500m_X7                          9.605945\n",
       "Lai_500m_X8                         15.747539\n",
       "Lai_500m_X9                         18.318241\n",
       "MCARI_X6                            -0.095959\n",
       "MCARI_X7                             0.022530\n",
       "MCARI_X8                             0.137190\n",
       "MCARI_X9                             0.217759\n",
       "MSR_X6                               0.626495\n",
       "MSR_X7                               1.219404\n",
       "MSR_X8                               1.389483\n",
       "NDVI_X6                              0.395715\n",
       "NDVI_X7                              0.558388\n",
       "OSAVI_X6                             0.452794\n",
       "OSAVI_X7                             0.641908\n",
       "OSAVI_X9                             0.773268\n",
       "SVAI_X7                              0.376536\n",
       "SVAI_X9                              0.457085\n",
       "WDRVI_X6                            -0.408118\n",
       "WDRVI_X8                            -0.097425\n",
       "WDVI_X6                              0.220377\n",
       "WDVI_X7                              0.273100\n",
       "WDVI_X8                              0.315481\n",
       "WDVI_X9                              0.315356\n",
       "wet_X9                              -0.105645\n",
       "Name: 10, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.iloc[j]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44ca6685-946f-49a4-aaf5-e2170736ceb0",
   "metadata": {},
   "source": [
    "#### 单个样本shap值可视化展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "14e3166d-c5a6-42f1-96df-417c1423af90",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script src=\"https://example.com/your-library.js\"></script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<script src=\"https://example.com/your-library.js\"></script>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74aae20e-9417-4545-9ef6-170f230d9921",
   "metadata": {},
   "source": [
    "<script src=\"https://example.com/your-library.js\"></script>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b677be0d-1575-4777-be81-f6869240525d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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c=n.getDerivedStateFromProps,f=\"function\"==typeof c||\"function\"==typeof o.getSnapshotBeforeUpdate;f||\"function\"!=typeof o.UNSAFE_componentWillReceiveProps&&\"function\"!=typeof o.componentWillReceiveProps||(u!==r||l!==s)&&Vi(t,o,r,s),Pi=!1;var p=t.memoizedState;o.state=p,Ri(t,r,o,a),l=t.memoizedState,u!==r||p!==l||Na.current||Pi?(\"function\"==typeof c&&(Ii(t,n,c,r),l=t.memoizedState),(u=Pi||Bi(t,n,u,r,p,l,s))?(f||\"function\"!=typeof o.UNSAFE_componentWillMount&&\"function\"!=typeof o.componentWillMount||(\"function\"==typeof o.componentWillMount&&o.componentWillMount(),\"function\"==typeof o.UNSAFE_componentWillMount&&o.UNSAFE_componentWillMount()),\"function\"==typeof o.componentDidMount&&(t.flags|=4194308)):(\"function\"==typeof o.componentDidMount&&(t.flags|=4194308),t.memoizedProps=r,t.memoizedState=l),o.props=r,o.state=l,o.context=s,r=u):(\"function\"==typeof 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t=e.stateNode;t.pendingContext?Aa(0,t.pendingContext,t.pendingContext!==t.context):t.context&&Aa(0,t.context,!1),ro(e,t.containerInfo)}function zu(e,t,n,r,a){return di(),hi(a),t.flags|=256,wu(e,t,n,r),t.child}var Lu,Ou,Au,Fu,Du={dehydrated:null,treeContext:null,retryLane:0};function Ru(e){return{baseLanes:e,cachePool:null,transitions:null}}function ju(e,t,n){var r,a=t.pendingProps,o=uo.current,u=!1,l=0!=(128&t.flags);if((r=l)||(r=(null===e||null!==e.memoizedState)&&0!=(2&o)),r?(u=!0,t.flags&=-129):null!==e&&null===e.memoizedState||(o|=1),Ca(uo,1&o),null===e)return 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i=e.memoizedState;null===i?e.memoizedState={isBackwards:t,rendering:null,renderingStartTime:0,last:r,tail:n,tailMode:a}:(i.isBackwards=t,i.rendering=null,i.renderingStartTime=0,i.last=r,i.tail=n,i.tailMode=a)}function Wu(e,t,n){var r=t.pendingProps,a=r.revealOrder,i=r.tail;if(wu(e,t,r.children,n),0!=(2&(r=uo.current)))r=1&r|2,t.flags|=128;else{if(null!==e&&0!=(128&e.flags))e:for(e=t.child;null!==e;){if(13===e.tag)null!==e.memoizedState&&$u(e,n,t);else if(19===e.tag)$u(e,n,t);else if(null!==e.child){e.child.return=e,e=e.child;continue}if(e===t)break e;for(;null===e.sibling;){if(null===e.return||e.return===t)break e;e=e.return}e.sibling.return=e.return,e=e.sibling}r&=1}if(Ca(uo,r),0==(1&t.mode))t.memoizedState=null;else switch(a){case\"forwards\":for(n=t.child,a=null;null!==n;)null!==(e=n.alternate)&&null===lo(e)&&(a=n),n=n.sibling;null===(n=a)?(a=t.child,t.child=null):(a=n.sibling,n.sibling=null),Bu(t,!1,a,n,i);break;case\"backwards\":for(n=null,a=t.child,t.child=null;null!==a;){if(null!==(e=a.alternate)&&null===lo(e)){t.child=a;break}e=a.sibling,a.sibling=n,n=a,a=e}Bu(t,!0,n,null,i);break;case\"together\":Bu(t,!1,null,null,void 0);break;default:t.memoizedState=null}return t.child}function Vu(e,t){0==(1&t.mode)&&null!==e&&(e.alternate=null,t.alternate=null,t.flags|=2)}function Hu(e,t,n){if(null!==e&&(t.dependencies=e.dependencies),Rl|=t.lanes,0==(n&t.childLanes))return null;if(null!==e&&t.child!==e.child)throw Error(i(153));if(null!==t.child){for(n=As(e=t.child,e.pendingProps),t.child=n,n.return=t;null!==e.sibling;)e=e.sibling,(n=n.sibling=As(e,e.pendingProps)).return=t;n.sibling=null}return t.child}function qu(e,t){if(!ai)switch(e.tailMode){case\"hidden\":t=e.tail;for(var n=null;null!==t;)null!==t.alternate&&(n=t),t=t.sibling;null===n?e.tail=null:n.sibling=null;break;case\"collapsed\":n=e.tail;for(var r=null;null!==n;)null!==n.alternate&&(r=n),n=n.sibling;null===r?t||null===e.tail?e.tail=null:e.tail.sibling=null:r.sibling=null}}function Qu(e){var t=null!==e.alternate&&e.alternate.child===e.child,n=0,r=0;if(t)for(var a=e.child;null!==a;)n|=a.lanes|a.childLanes,r|=14680064&a.subtreeFlags,r|=14680064&a.flags,a.return=e,a=a.sibling;else for(a=e.child;null!==a;)n|=a.lanes|a.childLanes,r|=a.subtreeFlags,r|=a.flags,a.return=e,a=a.sibling;return e.subtreeFlags|=r,e.childLanes=n,t}function Yu(e,t,n){var r=t.pendingProps;switch(ti(t),t.tag){case 2:case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:return Qu(t),null;case 1:case 17:return La(t.type)&&Oa(),Qu(t),null;case 3:return 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\"53\": {\"effect\": -0.10394208878278732, \"value\": -0.40811800575}, \"54\": {\"effect\": -1.2906217575073242, \"value\": -0.097425328}, \"55\": {\"effect\": -90.21587371826172, \"value\": 0.220377232919947}, \"56\": {\"effect\": -8.420839309692383, \"value\": 0.273100111107868}, \"57\": {\"effect\": 481.6496887207031, \"value\": 0.31548105639085}, \"58\": {\"effect\": 116.75495147705078, \"value\": 0.315356243968836}, \"59\": {\"effect\": -20.475627899169922, \"value\": -0.105644988}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
       "    document.getElementById('iQG1YRU9VQQAN3D0OIJE3')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<shap.plots._force.AdditiveForceVisualizer at 0x1965c6ecf40>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap.initjs()\n",
    "shap.force_plot(explainer.expected_value,shap_values[j],x_test.iloc[j])\n",
    "#shap.initjs()\n",
    "#shap.force_plot(explainer.expected_value,shap_values[j],x_test.iloc[j],matplotlib=True, link=\"identity\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "13e0f2fa-31d7-4273-83d9-7e7924c3799d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "plt.rcParams['font.family']='SimSun'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cd1df4ed-df5b-4126-b620-793f6e982402",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "plt.rcParams['font.size']=60"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83098351-3a23-40ec-8aa6-6c243f7b161a",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 对特征总体分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1210a6a0-4f77-4070-a8c7-bd9aee4feaeb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Glyph 8722 (\\N{MINUS SIGN}) missing from current font.\n",
      "Glyph 8722 (\\N{MINUS SIGN}) missing from current font.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x950 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(shap_values,x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bae1f10b-a47f-4fb2-9062-4809f59ee0b6",
   "metadata": {},
   "source": [
    "#### 特征重要性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "27dc5eea-3746-4f60-836b-b287ece40534",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x950 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(shap_values,x_test,plot_type = \"bar\",cmap='Greys')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7f7fab7-9abe-4b34-bd3a-baffa968c6ba",
   "metadata": {},
   "source": [
    "#### 部份依赖图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2f23d0e4-185a-4a7e-a1ab-38ceb73ffb54",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Glyph 8722 (\\N{MINUS SIGN}) missing from current font.\n",
      "Glyph 8722 (\\N{MINUS SIGN}) missing from current font.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.dependence_plot('AvgSurfT_inst_X11',shap_values,x_test,interaction_index = 'NAME',show = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "858d968e-04d8-4259-a027-c8ff234e4d50",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "shap.dependence_plot('SoilMoi0_10cm_inst_chousui',shap_values,x_test,interaction_index ='NAME',show = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eadfceca-684b-4782-8ad9-5d550f7479e7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "shap.dependence_plot('NAME',shap_values,x_test,interaction_index = None,show = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "017a726b-dba9-4f77-80b2-8f310f863b7b",
   "metadata": {},
   "source": [
    "#### 多个变量的交互分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "341a0914-16f4-4e5b-913c-71c79b396e5a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "shap_interaction_values = shap.TreeExplainer(xgboost_model_fit).shap_interaction_values(x_test)\n",
    "shap.summary_plot(shap_interaction_values, x_test, max_display=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "688e73af-02f2-4dad-8930-2d105b45fd00",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "shap.dependence_plot('WDVI_chousui',shap_values,x_test,interaction_index = 'NAME',show = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c95f71c2-9e0c-4f49-b866-551c80464346",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# x轴数据\n",
    "x = range(1, len(result_1['shap预测值']) + 1)\n",
    "plt.figure(figsize=(10, 6))\n",
    "# 绘制折线图\n",
    "plt.plot(x, result_1['shap预测值'], label='SHAP Predictions', marker='o')\n",
    "plt.plot(x, result_1['xgb预测值'], label='XGBoost Predictions', marker='o')\n",
    "plt.plot(x, result_1['真实值'], label='True Values', marker='o')\n",
    "\n",
    "# 添加图例、标题和轴标签\n",
    "plt.legend()\n",
    "plt.title('Predictions vs True Values')\n",
    "plt.xlabel('Data Points')\n",
    "plt.ylabel('Values')\n",
    "plt.ylim(0, 1400)\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7474cbf-1dce-4361-abbf-ed18d76b3f6d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_shap=result_1['shap预测值']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a990dabb-e83c-42ab-9062-818689ffb338",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_xgb=result_1['xgb预测值']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "378dda5a-79c6-4484-bc2f-51d8c3397c98",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 计算均方根误差（RMSE）\n",
    "rmse = mean_squared_error(y_xgb, y_shap, squared=False)\n",
    "# 计算平均绝对误差（MAE）\n",
    "mae = mean_absolute_error(y_xgb, y_shap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56e6f3ab-d287-431d-88b7-64e53d1b9f82",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8a8c35d-1810-4817-b8f1-56ae0f9469e4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c35c2a8f-8861-4d76-b601-17cf108190db",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
